scholarly journals Asymmetric effective connectivity within frontoparietal motor network underlying motor imagery and motor execution

2020 ◽  
Author(s):  
Takeshi Ogawa ◽  
Hideki Shimobayashi ◽  
Jun-ichiro Hirayama ◽  
Motoaki Kawanabe
Keyword(s):  
Motor Imagery ◽  
Primary Motor Cortex ◽  
Brain Activity ◽  
Effective Connectivity ◽  
Premotor Cortex ◽  
Brain Regions ◽  
Neural Representation ◽  
Motor Execution ◽  
Positive Effects ◽  
Motor Network

AbstractBoth imagery and execution of motor controls consist of interactions within a neuronal network, including frontal motor-related regions and posterior parietal regions. To reveal neural representation in the frontoparietal motor network, several approaches have been proposed: one is decoding of actions/modes related to motor control from the spatial pattern of brain activity; another is to estimate effective connectivity, which means a directed association between two brain regions within motor regions. However, a motor network consisting of multiple brain regions has not been investigated to illustrate network representation depending on motor imagery (MI) or motor execution (ME). Here, we attempted to differentiate the frontoparietal motor-related networks based on the effective connectivity in the MI and ME conditions. We developed a delayed sequential movement and imagery (dSMI) task to evoke brain activity associated with data under ME and MI in functional magnetic resonance imaging (fMRI) scanning. We applied a linear non-Gaussian acyclic causal model to identify effective connectivity among the frontoparietal motor-related brain regions for each condition. We demonstrated higher effective connectivity from the contralateral dorsal premotor cortex (dPMC) to the primary motor cortex (M1) in ME than in MI. We mainly identified significant direct effects of dPMC and ventral premotor cortex (vPMC) to the parietal regions. In particular, connectivity from the dPMC to the superior parietal lobule (SPL) in the same hemisphere showed significant positive effects across all conditions. Instead, interlateral connectivities from vPMC to SPL showed significantly negative effects across all conditions. Finally, we found positive effects from A1 to M1 in the same hemisphere, such as the audio motor pathway. These results indicated that sources of motor command originated from d/vPMC and influenced M1 as achievements of ME and MI, and the parietal regions as integration of somatosensory and visual representation during finger tapping. In addition, sequential sounds may functionally facilitate temporal motor processes.

Connectivity-Related Roles of Contralesional Brain Regions for Motor Performance Early after Stroke

2020 ◽  
Author(s):  
Lukas Hensel ◽  
Caroline Tscherpel ◽  
Jana Freytag ◽  
Stella Ritter ◽  
Anne K Rehme ◽  
...  
Keyword(s):  
Neural Activity ◽  
Motor Performance ◽  
Primary Motor Cortex ◽  
Effective Connectivity ◽  
Premotor Cortex ◽  
Brain Regions ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Anterior Intraparietal Sulcus

Abstract Hemiparesis after stroke is associated with increased neural activity not only in the lesioned but also in the contralesional hemisphere. While most studies have focused on the role of contralesional primary motor cortex (M1) activity for motor performance, data on other areas within the unaffected hemisphere are scarce, especially early after stroke. We here combined functional magnetic resonance imaging (fMRI) and transcranial magnetic stimulation (TMS) to elucidate the contribution of contralesional M1, dorsal premotor cortex (dPMC), and anterior intraparietal sulcus (aIPS) for the stroke-affected hand within the first 10 days after stroke. We used “online” TMS to interfere with neural activity at subject-specific fMRI coordinates while recording 3D movement kinematics. Interfering with aIPS activity improved tapping performance in patients, but not healthy controls, suggesting a maladaptive role of this region early poststroke. Analyzing effective connectivity parameters using a Lasso prediction model revealed that behavioral TMS effects were predicted by the coupling of the stimulated aIPS with dPMC and ipsilesional M1. In conclusion, we found a strong link between patterns of frontoparietal connectivity and TMS effects, indicating a detrimental influence of the contralesional aIPS on motor performance early after stroke.

scholarly journals Brain Activation in Primary Motor and Somatosensory Cortices during Motor Imagery Correlates with Motor Imagery Ability in Stroke Patients

2012 ◽  
Vol 2012 ◽  
pp. 1-17 ◽  
Author(s):  
Linda Confalonieri ◽  
Giuseppe Pagnoni ◽  
Lawrence W. Barsalou ◽  
Justin Rajendra ◽  
Simon B. Eickhoff ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Brain Activity ◽  
Premotor Cortex ◽  
Region Of Interest ◽  
Wave Force ◽  
Sine Wave ◽  
Lower Percentage ◽  
Imagery Ability ◽  
Motor Execution ◽  
Stroke Patients

Aims. While studies on healthy subjects have shown a partial overlap between the motor execution and motor imagery neural circuits, few have investigated brain activity during motor imagery in stroke patients with hemiparesis. This work is aimed at examining similarities between motor imagery and execution in a group of stroke patients. Materials and Methods. Eleven patients were asked to perform a visuomotor tracking task by either physically or mentally tracking a sine wave force target using their thumb and index finger during fMRI scanning. MIQ-RS questionnaire has been administered. Results and Conclusion. Whole-brain analyses confirmed shared neural substrates between motor imagery and motor execution in bilateral premotor cortex, SMA, and in the contralesional inferior parietal lobule. Additional region of interest-based analyses revealed a negative correlation between kinaesthetic imagery ability and percentage BOLD change in areas 4p and 3a; higher imagery ability was associated with negative and lower percentage BOLD change in primary sensorimotor areas during motor imagery.

Abstract 25: Periinfarct Rewiring Supports Recovery After Primary Motor Cortex Stroke

Stroke ◽  
2021 ◽  
Vol 52 (Suppl_1) ◽  
Author(s):  
Mitsouko van Assche ◽  
Elisabeth Dirren ◽  
Alexia Bourgeois ◽  
Andreas Kleinschmidt ◽  
Jonas Richiardi ◽  
...  
Keyword(s):  
Motor Cortex ◽  
Primary Motor Cortex ◽  
Spatial Scales ◽  
Premotor Cortex ◽  
Brain Regions ◽  
Relative Increase ◽  
Motor Recovery ◽  
The Core ◽  
Hand Dexterity ◽  
Motor Network

Background and Purpose: After stroke restricted to the primary motor cortex (M1), it is uncertain whether network reorganization associated with motor recovery involves the periinfarct or more remote brain regions. In humans, the challenge is to recruit patients with similar lesions in size and location. Methods: We studied 16 patients with focal M1 stroke and hand paresis. Motor function and resting-state MRI functional connectivity (FC) were studied at three time points: acute (<10 days), early subacute (3 weeks), and late subacute (3 months). FC correlates of motor recovery were investigated at three spatial scales, i) ipsilesional non-infarcted M1, ii) core motor network (including M1, premotor cortex (PMC), supplementary motor area (SMA), and primary somatosensory cortex), and iii) extended motor network including all regions structurally connected to the upper limb representation of M1. Results: Hand dexterity was impaired only in the acute phase ( P =0.036). At a small spatial scale, improved dexterity was associated with increased FC involving mainly the ipsilesional non-infarcted M1 and contralesional motor regions (cM1: rho=0.732; P =0.004; cPMC: rho=0.837, P <0.001; cSMA: rho=0.736; P =0.004). At a larger scale, motor recovery correlated with the relative increase in total FC strength in the core motor network compared to the extended motor network (rho=0.71; P =0.006). Conclusions: FC changes associated with motor improvement involve the perilesional M1 and do not extend beyond the core motor network. The ipsilesional non-infarcted M1 and core motor regions could hence be primary targets for future restorative therapies.

scholarly journals Cortical activation associated with motor preparation can be used to predict the freely chosen effector of an upcoming movement and reflects response time: An fMRI decoding study

2018 ◽  
Author(s):  
Satoshi Hirose ◽  
Isao Nambu ◽  
Eiichi Naito
Keyword(s):  
Response Time ◽  
Primary Motor Cortex ◽  
Response Times ◽  
Brain Activity ◽  
Premotor Cortex ◽  
Brain Regions ◽  
Cortical Activation ◽  
List Type ◽  
Action Execution ◽  
Motor Representations

AbstractMotor action is prepared in the human brain for rapid initiation at the appropriate time. Recent non-invasive decoding techniques have shown that brain activity for action preparation represents various parameters of an upcoming action. In the present study, we demonstrated that a freely chosen effector can be predicted from brain activity measured using functional magnetic resonance imaging (fMRI) before initiation of the action. Furthermore, the activity was related to response time (RT). We measured brain activity with fMRI while 12 participants performed a finger-tapping task using either the left or right hand, which was freely chosen by them. Using fMRI decoding, we identified brain regions in which activity during the preparatory period could predict the hand used for the upcoming action. We subsequently evaluated the relationship between brain activity and the RT of the upcoming action to determine whether correct decoding was associated with short RT. We observed that activity in the supplementary motor area, dorsal premotor cortex, and primary motor cortex measured before action execution predicted the hand used to perform the action with significantly above-chance accuracy (approximately 70%). Furthermore, in most participants, the RT was shorter in trials for which the used hand was correctly predicted. The present study showed that preparatory activity in cortical motor areas represents information about the effector used for an upcoming action, and that well-formed motor representations in these regions are associated with reduced response times.HighlightsBrain activity measured by fMRI was used to predict freely chosen effectors.M1/PMd and SMA activity predicted the effector hand prior to action initiation.Response time was shorter in trials in which effector hand was correctly predicted.Freely chosen action is represented in the M1/PMd and SMA.Well-formed preparatory motor representations lead to reduced response time.

scholarly journals Mapping Human Laryngeal Motor Cortex during Vocalization

2020 ◽  
Vol 30 (12) ◽  
pp. 6254-6269 ◽  
Author(s):  
Nicole Eichert ◽  
Daniel Papp ◽  
Rogier B Mars ◽  
Kate E Watkins
Keyword(s):  
Motor Cortex ◽  
Primary Motor Cortex ◽  
Brain Activity ◽  
Premotor Cortex ◽  
Neural Representation ◽  
Functional Magnetic Resonance ◽  
Human Larynx ◽  
Motor Representations ◽  
Quantitative Neuroimaging ◽  
Hand Area

Abstract The representations of the articulators involved in human speech production are organized somatotopically in primary motor cortex. The neural representation of the larynx, however, remains debated. Both a dorsal and a ventral larynx representation have been previously described. It is unknown, however, whether both representations are located in primary motor cortex. Here, we mapped the motor representations of the human larynx using functional magnetic resonance imaging and characterized the cortical microstructure underlying the activated regions. We isolated brain activity related to laryngeal activity during vocalization while controlling for breathing. We also mapped the articulators (the lips and tongue) and the hand area. We found two separate activations during vocalization—a dorsal and a ventral larynx representation. Structural and quantitative neuroimaging revealed that myelin content and cortical thickness underlying the dorsal, but not the ventral larynx representation, are similar to those of other primary motor representations. This finding confirms that the dorsal larynx representation is located in primary motor cortex and that the ventral one is not. We further speculate that the location of the ventral larynx representation is in premotor cortex, as seen in other primates. It remains unclear, however, whether and how these two representations differentially contribute to laryngeal motor control.

scholarly journals Mapping human laryngeal motor cortex during vocalization

2020 ◽  
Author(s):  
Nicole Eichert ◽  
Daniel Papp ◽  
Rogier B. Mars ◽  
Kate E. Watkins
Keyword(s):  
Motor Control ◽  
Motor Cortex ◽  
Primary Motor Cortex ◽  
Brain Activity ◽  
Premotor Cortex ◽  
Neural Representation ◽  
Human Larynx ◽  
Motor Representations ◽  
Quantitative Neuroimaging ◽  
Hand Area

AbstractThe representations of the articulators involved in human speech production are organized somatotopically in primary motor cortex. The neural representation of the larynx, however, remains debated. Both a dorsal and a ventral larynx representation have been previously described. It is unknown, however, whether both representations are located in primary motor cortex. Here, we mapped the motor representations of the human larynx using fMRI and characterized the cortical microstructure underlying the activated regions. We isolated brain activity related to laryngeal activity during vocalization while controlling for breathing. We also mapped the articulators (the lips and tongue) and the hand area. We found two separate activations during vocalization – a dorsal and a ventral larynx representation. Structural and quantitative neuroimaging revealed that myelin content and cortical thickness underlying the dorsal, but not the ventral larynx representation, are similar to those of other primary motor representations. This finding confirms that the dorsal larynx representation is located in primary motor cortex and that the ventral one is not. We further speculate that the location of the ventral larynx representation is in premotor cortex, as seen in other primates. It remains unclear, however, whether and how these two representations differentially contribute to laryngeal motor control.

scholarly journals Conditional Granger Causality Analysis of Effective Connectivity during Motor Imagery and Motor Execution in Stroke Patients

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Li Wang ◽  
Jingna Zhang ◽  
Ye Zhang ◽  
Rubing Yan ◽  
Hongliang Liu ◽  
...  
Keyword(s):  
Granger Causality ◽  
Motor Function ◽  
Motor Imagery ◽  
Effective Connectivity ◽  
Causality Analysis ◽  
Motor Execution ◽  
Stroke Patients ◽  
Granger Causality Analysis ◽  
Cortical Motor ◽  
Motor Network

Aims.Motor imagery has emerged as a promising technique for the improvement of motor function following stroke, but the mechanism of functional network reorganization in patients during this process remains unclear. The aim of this study is to evaluate the cortical motor network patterns of effective connectivity in stroke patients.Methods.Ten stroke patients with right hand hemiplegia and ten normal control subjects were recruited. We applied conditional Granger causality analysis (CGCA) to explore and compare the functional connectivity between motor execution and motor imagery.Results.Compared with the normal controls, the patient group showed lower effective connectivity to the primary motor cortex (M1), the premotor cortex (PMC), and the supplementary motor area (SMA) in the damaged hemisphere but stronger effective connectivity to the ipsilesional PMC and M1 in the intact hemisphere during motor execution. There were tighter connections in the cortical motor network in the patients than in the controls during motor imagery, and the patients showed more effective connectivity in the intact hemisphere.Conclusions.The increase in effective connectivity suggests that motor imagery enhances core corticocortical interactions, promotes internal interaction in damaged hemispheres in stroke patients, and may facilitate recovery of motor function.

scholarly journals Effective connectivity differences in motor network during passive movement of paretic and non-paretic ankles in subacute stroke patients

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8942
Author(s):  
Marianna Nagy ◽  
Csaba Aranyi ◽  
Gábor Opposits ◽  
Tamás Papp ◽  
Levente Lánczi ◽  
...  
Keyword(s):  
Network Analysis ◽  
Primary Motor Cortex ◽  
Effective Connectivity ◽  
Premotor Cortex ◽  
Passive Movement ◽  
Causal Modeling ◽  
Hemodynamic Parameters ◽  
Stroke Patients ◽  
Subacute Stroke ◽  
Motor Network

Background A better understanding of the neural changes associated with paresis in stroke patients could have important implications for therapeutic approaches. Dynamic Causal Modeling (DCM) for functional magnetic resonance imaging (fMRI) is commonly used for analyzing effective connectivity patterns of brain networks due to its significant property of modeling neural states behind fMRI signals. We applied this technique to analyze the differences between motor networks (MNW) activated by continuous passive movement (CPM) of paretic and non-paretic ankles in subacute stroke patients. This study aimed to identify CPM induced connectivity characteristics of the primary sensory area (S1) and the differences in extrinsic directed connections of the MNW and to explain the hemodynamic differences of brain regions of MNW. Methods For the network analysis, we used ten stroke patients’ task fMRI data collected under CPMs of both ankles. Regions for the MNW, the primary motor cortex (M1), the premotor cortex (PM), the supplementary motor area (SMA) and the S1 were defined in a data-driven way, by independent component analysis. For the network analysis of both CPMs, we compared twelve models organized into two model-families, depending on the S1 connections and input stimulus modeling. Using DCM, we evaluated the extrinsic connectivity strengths and hemodynamic parameters of both stimulations of all patients. Results After a statistical comparison of the extrinsic connections and their modulations of the “best model”, we concluded that three contralateral self-inhibitions (cM1, cS1 and cSMA), one contralateral inter-regional connection (cSMA→cM1), and one interhemispheric connection (cM1→iM1) were significantly different. Our research shows that hemodynamic parameters can be estimated with the Balloon model using DCM but the parameters do not change with stroke. Conclusions Our results confirm that the DCM-based connectivity analyses combined with Bayesian model selection may be a useful technique for quantifying the alteration or differences in the characteristics of the motor network in subacute stage stroke patients and in determining the degree of MNW changes.

scholarly journals Periinfarct rewiring supports recovery after primary motor cortex stroke

2021 ◽  
pp. 0271678X2110029
Author(s):  
Mitsouko van Assche ◽  
Elisabeth Dirren ◽  
Alexia Bourgeois ◽  
Andreas Kleinschmidt ◽  
Jonas Richiardi ◽  
...  
Keyword(s):  
Motor Cortex ◽  
Primary Motor Cortex ◽  
Spatial Scales ◽  
Premotor Cortex ◽  
Core Network ◽  
Small Spatial Scale ◽  
The Core ◽  
Hand Dexterity ◽  
Motor Network ◽  
Motor Improvement

After stroke restricted to the primary motor cortex (M1), it is uncertain whether network reorganization associated with recovery involves the periinfarct or more remote regions. We studied 16 patients with focal M1 stroke and hand paresis. Motor function and resting-state MRI functional connectivity (FC) were assessed at three time points: acute (<10 days), early subacute (3 weeks), and late subacute (3 months). FC correlates of recovery were investigated at three spatial scales, (i) ipsilesional non-infarcted M1, (ii) core motor network (M1, premotor cortex (PMC), supplementary motor area (SMA), and primary somatosensory cortex), and (iii) extended motor network including all regions structurally connected to the upper limb representation of M1. Hand dexterity was impaired only in the acute phase ( P = 0.036). At a small spatial scale, clinical recovery was more frequently associated with connections involving ipsilesional non-infarcted M1 (Odds Ratio = 6.29; P = 0.036). At a larger scale, recovery correlated with increased FC strength in the core network compared to the extended motor network (rho = 0.71; P = 0.006). These results suggest that FC changes associated with motor improvement involve the perilesional M1 and do not extend beyond the core motor network. Core motor regions, and more specifically ipsilesional non-infarcted M1, could hence become primary targets for restorative therapies.

scholarly journals Voluntary Movements Discipline Audiovisual Perception

2020 ◽  
Author(s):  
Ahmad Yousef
Keyword(s):  
Red Blood Cells ◽  
Blood Cells ◽  
Primary Motor Cortex ◽  
Premotor Cortex ◽  
Brain Regions ◽  
Voluntary Movements ◽  
Deep Breathing ◽  
Audiovisual Perception ◽  
Cognitive Action ◽  
Actual Material

We had shown that deep breathing had been able to effectively and timely alter visual and auditory bistable perception, see reference 1, 2. Deep breathing requires cognitive control, and therefore, in this study, we decide to investigate whether voluntary movements of human hands are able to govern the audiovisual perception using an integrative stimulus that’s built up with the aforementioned visual and auditory stimuli. Astoundingly, when the human subjects moves the pen towards the actual physical direction, even without touching the screen; the original materials of the audiovisual stimulus appear. Reversed perception, namely, illusory motion reversals and illusory word appear when the pen is moved in the opposite direction of the actual motion. Cognitive actions’ brain areas, namely, dorsolateral prefrontal cortex, premotor cortex, and primary motor cortex may require high concentration of oxygenated hobgoblin red blood cells to achieve fulsome executive movements; and this could results in significant reduction of the concentrations of the oxygenated hobgoblin red blood cells in the visual and auditory cortices. Reductions that disallow one of two; the central versus the peripheral conscious brains dedicated for audiovisual perceptions, to rapidly alternate their conscious productions; and therefore, stoppage against bistable audiovisual perception will occur. We thus hypothesis that the DLPFC may send signals to deactivate the peripheral areas in the sensory brain regions when the cognitive action is harmonized with the actual material; but it may send a contrary signal to deactivate the central areas in the sensory brain regions when the cognitive action and the actual material are disharmonized.

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